The application of Internet of Things (IoT) and Machine Learning (ML) to the agricultural industry has enabled the development and creation of smart farms and precision agriculture. The growth in the number of smart farms and potential cooperation between these farms has given rise to the Cooperative Smart Farming (CSF) where different connected farms collaborate with each other and share data for their mutual benefit. This data sharing through CSF has various advantages where individual data from separate farms can be aggregated by ML models and be used to produce actionable outputs which then can be utilized by all the farms in CSFs. This enables farms to gain better insights for enhancing desired outputs, such as crop yield, managing water resources and irrigation schedules, as well as better seed applications. However, complications may arise in CSF when some of the farms do not transfer high-quality data and rather rely on other farms to feed ML models. Another possibility is the presence of rogue farms in CSFs that want to snoop on other farms without actually contributing any data. In this paper, we analyze the behavior of farms participating in CSFs using game theory approach, where each farm is motivated to maximize its profit. We first present the problem of defective farms in CSFs due to lack of better data, and then propose a ML framework that segregates farms and automatically assign them to an appropriate CSF cluster based on the quality of data they provide. Our proposed model rewards the farms supplying better data and penalize the ones that do not provide required data or are malicious in nature, thus, ensuring the model integrity and better performance all over while solving the defective farms problem.

中文翻译：

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合作式智能农业的博弈分析

物联网（IoT）和机器学习（ML）在农业领域的应用使智能农场和精确农业的开发和创造成为可能。智能农场数量的增长以及这些农场之间潜在的合作关系导致了合作智能农场（CSF），在该农场中，不同的互联农场相互协作并共享数据以互惠互利。通过CSF进行的数据共享具有多种优势，其中可以通过ML模型汇总来自单独服务器场的单个数据，并用于产生可操作的输出，然后这些输出可以被CSF中的所有服务器场使用。这使农场能够获得更好的见识，以提高所需的产量，例如作物产量，管理水资源和灌溉时间表以及更好地应用种子。然而，当某些服务器场不传输高质量数据，而是依靠其他服务器场提供ML模型时，CSF可能会出现复杂的情况。另一个可能性是在CSF中存在流氓服务器场，它们想要监听其他服务器场而实际上不提供任何数据。在本文中，我们使用博弈论方法分析了参与CSF的农场的行为，每个农场都被激励最大化其利润。我们首先提出由于缺少更好的数据而导致CSF中的服务器场存在缺陷的问题，然后提出一个ML框架，该ML框架将服务器场隔离，并根据它们提供的数据质量自动将它们分配给适当的CSF群集。我们提出的模型会奖励提供更好数据的农场，并对那些没有提供所需数据或本质上是恶意的农场进行惩罚，因此，